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Attack -Tolerant Distributed Sensing for Dynamic Spectrum Access Networks

Attack -Tolerant Distributed Sensing for Dynamic Spectrum Access Networks. Alexander W. Min, Kang G. Shin, and Xin Hu Real-Time Computing Laboratory (RTCL) The University of Michigan. Spectrum is scarce resource. Source: Federal Communications Commission (FCC).

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Attack -Tolerant Distributed Sensing for Dynamic Spectrum Access Networks

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  1. Attack-Tolerant Distributed Sensing forDynamic Spectrum Access Networks Alexander W. Min, Kang G. Shin, and XinHu Real-Time Computing Laboratory (RTCL) The University of Michigan

  2. Spectrum is scarce resource Source: Federal Communications Commission (FCC)

  3. But, severely under-utilized 5.2 % Source: Shared Spectrum Company

  4. A new paradigm – Dynamic Spectrum Access spectrum sensing - key enabling technology

  5. Need for cooperative sensing + + 2 sec. 10 % -20 dB Single sensor with one-time sensing is NOT enough

  6. BUT, can we really trust sensors? Sensors can be mal-functioning or compromised

  7. This paper How to design secure cooperative sensing?

  8. System model • IEEE 802.22 WRAN • • 1st CR-based international standard • • Goal : provide wireless broadband access • in rural areas by reusing TV bands • Signal propagation model • • Received signal strength (RSS) • log-normal shadowing • Spectrum sensing model • • Energy detection (ED) • -- Simple,BUT inaccurate • -- ED’s output: Estimation of RSSs

  9. Spatially-correlated shadow fading • Construction of shadowing field Accurate modeling of realistic shadowing environments

  10. Attack model • Attack scenarios • • A sensor is mal-functioning • Distorted sensor reports • •A sensor is compromised • Attack types • • TYPE1: increase false-alarm rate by increasing RSS • • TYPE2: increase miss-detection rate by decreasing RSS • Final sensor reports • • Sensor report = Energy detector’s output + distortion GOAL: Detection of any abnormal sensor reports

  11. Cooperative sensing in 802.22 A D B C > > < 10 (threshold) Report(A) + Report(B) + Report(C) + Report(D) = 11 5 12 decision statistic PU activity : ON Type-1 Type-2 OFF Decision : 0 1 1

  12. Key features: Attack-Tolerant Distributed Sensing • Exploits shadow fading correlation in RSSs • Proposes cluster-based cooperative sensing • Safeguards both type-1 and type-2 of attacks • Employs weighted gain combining (WGC) Shadow fading correlation can be exploited to filter out abnormal sensing reports

  13. Our approach: (1) sensor selection • Q1)How to select sensors? • sensor diversity • correlation profile • Independent sensors • Correlatedsensors • [Visotskyet al. 05] • [Ghasemiet al. 07] • [Selenet al. 08] • “A double-edged sword”

  14. Our approach: (2) data fusion • Q2) How to make a final decision? Our focus

  15. Cross validation using correlation filter • Cooperative detection A F • • 0 : normal, 1: abnormal B E D … C sensor cluster Discard the sensor report if # of flags > threshold

  16. How to raise a flag? • Correlation filter design • • Compute conditional p.d.f. of sensor reports • -- Prob(sensor A’s report | sensor B’s report ) = ? • -- Shifted log-normal distribution • Key observation • Corr. shadow fading ≈ Corr. sensor reports abnormal abnormal • • Tradeoff in thresholds: over(under) filtering Correlation filter is efficient, but NOT perfect

  17. A new data-fusion rule • Weighted Gain Combining (WGC) • • Give different weights to sensor reports A B C D abnormal abnormal • • weight(A) > weight(B) > weight(C) > weight(D) = 0 • • Σweight(•) = 1 WGC further improves attack-tolerance

  18. Performance Evaluation • Simulation Setup • • 30 sensors in 5 clusters • • 10 compromised sensors • • Shadow fading σdB=4.5 dB • Testing schemes • 1) Equal gain combining (EGC) • 2) Statistic-based method (Outlier) [Kaligineediet al. 08] • – Reject any sensor reports outside range R • 3) Correlation Filter + EGC/WGC (ADSP)

  19. Impact of sensor clustering Clustering achieves 90 % detection performance Small performance degradation even under -23 dB

  20. ADSP successfully tolerates type-1 attacks EGC Outlier Filter + EGC High false-alarm Filter + WGC Low false-alarm Sensing scheduling can further improve attack-tolerance

  21. ADSP successfully tolerates type-2 attacks Filter + WGC Filter + EGC Tolerates both type-1 and type-2 attacks

  22. How to set the filter threshold? Detection False-alarm Tradeoff between false-alarm vs. detection rate

  23. Finding an optimal filter threshold Optimal filter threshold • Underfiltering • Overfiltering • • number of attackers • • attack strengths Optimal threshold exists in terms of # of valid sensors

  24. Summary & Future work • Attack-tolerant distributed sensing • Develops cluster-based cooperative sensing • Exploits shadow fading correlation • Proposes new date fusion • Future work • Optimal detection/attack strategy • Design of sensing scheduling

  25. Thank you Alexander W. Min alexmin@eecs.umich.edu Visit Real-Time Computing Lab (RTCL) http://kabru.eecs.umich.edu

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